The text provides an overview of machine learning, including its subfields such as supervised learning, unsupervised learning, and reinforcement learning. It also discusses the evolution of machine learning from its inception in the late 80s and early 90s to its current state, including its popular...
Gartner predicts that by 2022 there would be at least 40 % of new application
development project going on in the market that would be requiring machine learning
co-developers on their team. It 's expected that these project will generate a
revenue of around three point nine trillion dollar. Machine learning is a subfield
of artificial intelligence that focuses on the design of system that can learn from
and make decisions and predictions based on the experience which is data. Machine
learning enables computer to act and make data-driven decisions rather than Being
explicitly programmed to carry out a certain task these programs are designed to
learn and improve over time. Tamar used to train deep neural network to achieve
better accuracy in those cases where former was not performing up to the mark. Deep
learning is a subset of machine learning where similar machine learning is similar
to Tamar's. Tamar says deep learning can now scale up to massive data volumes. The
algorithm learns the input pattern that generate the output patterns.
Machine learning is called a supervised learning because the process of an
algorithm learning from the training data set can be thought of as a teacher
supervising the learning process if we know the correct answers. The result of
supervised learning process is a predictor model which is capable of associating a
label duck. Or not duck to the new image presented to the model. Once the model is
ready. It can easily predict the correct output of a never seen input in this
slide. The goal for unsupervised learning is to model the underlying structure or
distribution in the data in order to learn more about the data. Unsupervised
Learning is where you only have Put data X and no corresponding output variable.
The goal that applies to this task is clustering in this task similar data
instances are grouped together to identify clusters of data. The algorithm
processes an unlabeled training data set and based on the characteristics. It grips
the picture into three different clusters of data despite the ability of grouping
similar data into clusters. The algorithm is not capable to add labels to the crow.
It only knows which data instances are similar , but it can not identify the
meaning of this group. So these are called as unsupervised learning because unlike
supervised learning ever.
Reinforcement learning is a type of machine learning algorithm which allows
software agents and machine to automatically determine the ideal Behavior within a
specific context to maximize its performance. Pavlo trained his dog using
reinforcement learning or how he applied the reinforcement method to train his dog.
Babu integrated learning in four stages initially Pavlo gave me to his dog and in
response to the meet the dog started salivating next. The term artificial
intelligence was first coined in the year 1956. The concept is pretty old but it
has gained its popularity recently. It is expected that 70 % of the price will
Implement a i over the next 12 months which is up from 40 percent in 2016 and 51
percent in 2017. The AI and machine learning and deep learning are just the subset
of each other. Machine learning is a subset of AI which enables the computer to act
and make data-driven decisions to carry out a certain task. Machine learning came
into existence in the late 80s and the early 90s. Problem was how to efficiently
train large complex model in the field of computer science and artificial
intelligence. It had inherited from the AI and move towards the methods and model.
It borrowed from statistics and probability Theory.
Deep learning is a particular kind of machine learning that is inspired by the
functionality of our brain cells. It simply takes the data connection between all
the artificial neurons and adjust them according to the data pattern. More neurons
are added at the size of the data is large it automatically features learning at
multiple levels of abstraction. Thereby allowing a system to learn complex function
mapping without depending on any specific algorithm. Deep learning is machine
learning more specifically. It is the next evolution of machine learning. The most
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